Abstract
It is widely recognized that a firm’s financial behaviour is the result of a complex mix of conditions, both internal and external to the firm; these may affect its investment decisions and its growth opportunities. This paper offers a twofold contribution to the empirical debate on the financing of innovation. First of all, it provides a comprehensive descriptions of possible simultaneous patterns which may affect a firm’s relevant dimensions, namely innovation inputs, innovation output, leverage and profitability. By using a Granger-Causality framework we will show that a firm’s leverage does not cause innovation output, as proxied by a measure of a firm’s successful innovation, while it is rather caused by successful innovation and a firm’s operating profitability. The second contribution is an original investigation of the determinants of a firm’s capital structure based on a panel of Italian firms which links the third Community Innovation Survey with an administrative data source providing economic and financial information collected from balance sheets and income statements referring to the period 1996–2003. This paper provides support for the pecking order theory as our firms are less indebted when operating profitability increases, but the use of external funding increases with their innovative effort. We also find support for the existence of credit constrains which seem to affect small innovative firms when compared with larger enterprises.
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Notes
Innopolicy Trend Chart, Country report 2008, European Commission, Enterprise Directorate—General.
The Multiple Imputation Approach to the estimation of missing values for the innovation variable is focused in a specific chapter of my PhD thesis. Further details are available on request.
It is also worth underlining that this procedure has determined an overall good prediction of the innovation variable according with the standard measures used for logistic regressions (Percent concordant computed with the SAS Logistic procedure does never fall below the value of 70.8%). This procedure also meets the methodological requirements stressed by the MI literature (Schafer 1997; Rubin 1996), i.e. the need of using a rich set of covariates in the imputation model, particularly those which represent a specific focus of the research.
Bearing in mind the short time span of our panel, we considered the significance of further lags in order to select the appropriate lag structure.
Kazemi and Crouchley (2006) provide an historical review of the properties of modelling initial conditions, and also develop an empirical application for a panel of economic growth data, by using a variety of model specifications. They find that even though ignoring initial conditions results in an upward bias of the state dependence and a downward bias in the coefficients of the explanatory variables, models based on different assumptions regarding initial conditions provide rather different estimation results.
Although innovation inputs such as R&D expenditures are covered by the CIS survey, this information is not available for the entire period under analysis and thus is not suitable for causality tests.
Variable INN and INTANG_TA have been lagged one period in order to capture the effects of past decisions on both the investments in intangible assets such as R&D expenditures and the decision to introduce innovation.
It is worth stressing the fact that our data set is based on a balanced panel of firms which have operated continuously in the industrial sector during our time span. This characteristic makes it plausible to assume that our firms are, on average, of relatively good quality.
Small firms are those with no more than 50 employees; medium firms are those with between 50 and 500 employees, and large firms have more than 500 employees. Groups are determined on the basis of the average size during the period 1996–2003. Results do not change when allowing the firms to enter and exit the three size classes.
We achieve the same conclusion by using another specification (not reported) where the variable SIZE is included in the set of regressions by size class, as an additional test for the role of a firm’s reputation in explaining LEVERAGE. We found that the coefficient of SIZE is negative and significant for the groups of medium firms, whereas it is not for the group of large firms.
In fact, it is worth recalling that information collected with the CIS3 survey refers to the 3-year period 1998–2000, which falls in the middle of our panel. The implicit hypothesis is that this additional information at the firm level can reasonably be assumed to be time invariant during our time span.
The list of obstacles to innovation includes: ‘excessive perceived economic risks’, ‘innovation costs too high’ and ‘lack of source of finance’.
A higher coefficient associated with the ROS variable in the group of small innovative with respect the group of small non-innovative firms is also confirmed when using the panel data specification corresponding to model 3) (not shown here for the sake of brevity).
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Acknowledgments
A special thanks to Paul Stoneman for helpful comments on a earlier draft of this paper. I would also like to thank participants at the DRUID Summer Conference 2010, Imperial College Business School, London, at the Trans-Atlantic Doctoral Conference 2010, London Business School and two anonymous referees for their fruitful remarks and suggestions. Finally, I’d like to acknowledge the support by Giulio Perani, director of the Innovation and R&D Statistic Unit and Caterina Viviano, director of the methodological unit at the division for statistical registers and by the Directorate of Regional Offices of the National Institute of Statistics for providing access to the data set.
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Bartoloni, E. Capital structure and innovation: causality and determinants. Empirica 40, 111–151 (2013). https://doi.org/10.1007/s10663-011-9179-y
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DOI: https://doi.org/10.1007/s10663-011-9179-y